From 0c25e41dc1a59dd47720d4cd17ae45eb83d44420 Mon Sep 17 00:00:00 2001
From: Christopher Rhodes <christopher.rhodes@embl.de>
Date: Fri, 26 Apr 2024 14:28:12 +0200
Subject: [PATCH] Run pixel and object classification on ilastik projects' own
inputs
---
.../verify_multichannel_ilastik_inputs.py | 111 ++++++++++++++++++
1 file changed, 111 insertions(+)
create mode 100644 model_server/scripts/verify_multichannel_ilastik_inputs.py
diff --git a/model_server/scripts/verify_multichannel_ilastik_inputs.py b/model_server/scripts/verify_multichannel_ilastik_inputs.py
new file mode 100644
index 00000000..1159b3f1
--- /dev/null
+++ b/model_server/scripts/verify_multichannel_ilastik_inputs.py
@@ -0,0 +1,111 @@
+from pathlib import Path
+
+import h5py
+import numpy as np
+import pandas as pd
+
+from model_server.base.accessors import generate_file_accessor, write_accessor_data_to_file, InMemoryDataAccessor
+from model_server.extensions.ilastik.models import IlastikPixelClassifierModel, IlastikObjectClassifierFromPixelPredictionsModel
+
+def get_input_files(where_ilp: Path) -> list:
+ files = []
+ with h5py.File(where_ilp, 'r') as h5:
+ infos = h5['Input Data/infos']
+ for lane in infos.keys():
+ lane_dict = {}
+ for role in infos[lane].keys():
+ if len(infos[lane][role]) == 0:
+ continue
+ rel_path = Path(infos[lane][role]['filePath'][()].decode())
+ lane_dict[role] = where_ilp.parent / rel_path
+ files.append(lane_dict)
+ return files
+
+if __name__ == '__main__':
+ where_out = Path('c:/Users/rhodes/projects/proj0015-model-server/issues/0032_multiple_input_channels/output')
+ root = Path('w:/03_analysis/Trial3_LSM900')
+ max_files = 1
+ ilps = [
+ '01_ilastik_files/relpath_240301_LSM900_DNA_PC.ilp',
+ '01_ilastik_files/relpath_240320_LSM900_DNA_OC_new.ilp',
+ '01_ilastik_files/relpath_240301_LSM900_TM_PC.ilp',
+ '01_ilastik_files/relpath_240320_LSM900_TM_OC_new.ilp'
+ ]
+ records = []
+ for f in ilps:
+ ilp = root / f
+ assert ilp.exists()
+ outdir = where_out / ilp.stem
+ outdir.mkdir(parents=True, exist_ok=True)
+
+ if ilp.stem.upper().endswith('_PC'):
+ mod = IlastikPixelClassifierModel(
+ params={'project_file': str(ilp)},
+ enforce_embedded=False
+ )
+ infiles = get_input_files(ilp)
+ for ln in infiles[0:max_files]:
+ acc_raw = generate_file_accessor(root / ln['Raw Data'])
+ pxmap = mod.infer(acc_raw)[0]
+ pxmap_fn = 'pxmap_' + ln['Raw Data'].stem + '.tif'
+ write_accessor_data_to_file(outdir / pxmap_fn, pxmap)
+ record = {
+ 'classifier': str(ilp.relative_to(root)),
+ 'input_raw_data': str(ln['Raw Data'].relative_to(root)),
+ 'input_raw_data_chroma': acc_raw.chroma,
+ 'input_raw_data_dtype': acc_raw.dtype,
+ 'input_raw_data_shape_dict': acc_raw.shape_dict,
+ 'output_file': pxmap_fn,
+ 'output_dtype': pxmap.dtype,
+ 'output_chroma': pxmap.chroma,
+ 'output_shape_dict': pxmap.shape_dict,
+ }
+ records.append(record)
+
+ elif ilp.stem.upper().endswith('_OC_NEW'):
+ mod = IlastikObjectClassifierFromPixelPredictionsModel(
+ params={'project_file': str(ilp)},
+ enforce_embedded=False
+ )
+ infiles = get_input_files(ilp)
+ for ln in infiles[0:max_files]:
+ acc_raw = generate_file_accessor(root / ln['Raw Data'])
+ pa_pxmap = root / ln['Prediction Maps']
+
+ if pa_pxmap.parts[-2].upper().endswith('.H5'):
+ pa_h5f = root / Path(*pa_pxmap.parts[0:-1])
+ h5_key = pa_pxmap.parts[-1]
+ pxmap_data = h5py.File(pa_h5f)[h5_key][()] # C x Y x X ?
+ pxmap_yxc = np.moveaxis(
+ pxmap_data,
+ [1, 2, 0],
+ [0, 1, 2]
+ )
+ acc_pxmap = InMemoryDataAccessor(np.expand_dims(pxmap_yxc, -1))
+ else:
+ acc_pxmap = generate_file_accessor(pa_pxmap)
+ obmap = mod.infer(acc_raw, acc_pxmap)[0]
+ obmap_fn = 'obmap_' + ln['Raw Data'].stem + '.tif'
+ write_accessor_data_to_file(outdir / obmap_fn, obmap)
+ record = {
+ 'classifier': str(ilp.relative_to(root)),
+ 'input_raw_data': str(ln['Raw Data'].relative_to(root)),
+ 'input_raw_data_chroma': acc_raw.chroma,
+ 'input_raw_data_dtype': acc_raw.dtype,
+ 'input_raw_data_shape_dict': acc_raw.shape_dict,
+ 'input_pxmap': str(ln['Prediction Maps'].relative_to(root)),
+ 'input_pxmap_chroma': acc_pxmap.chroma,
+ 'input_pxmap_dtype': acc_pxmap.dtype,
+ 'input_pxmap_shape_dict': acc_pxmap.shape_dict,
+ 'output_file': obmap_fn,
+ 'output_dtype': obmap.dtype,
+ 'output_chroma': obmap.chroma,
+ 'output_shape_dict': obmap.shape_dict,
+ }
+ records.append(record)
+
+ else:
+ raise Exception(f'unidentified project file {ilp}')
+
+ pd.DataFrame(records).to_csv(where_out / 'record.csv', index=False)
+ print('Finished')
--
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